Structure-Based Drug Design
Applying computational chemistry techniques to accelerate drug discovery through molecular modeling, virtual screening, and structure-activity relationship studies.
The Challenge: Accelerating Drug Discovery
Traditional drug discovery is time-consuming and expensive, often taking 10-15 years and billions of dollars to bring a single drug to market. Computational approaches can significantly accelerate this process by predicting molecular interactions, optimizing lead compounds, and reducing the number of experimental candidates that need to be synthesized and tested.
Our Approach: Computational Drug Discovery
This research focuses on leveraging advanced computational chemistry methods to identify and optimize potential drug candidates. Through collaborations with pharmaceutical researchers, I apply molecular modeling techniques to understand drug-target interactions, predict pharmacological properties, and guide experimental synthesis efforts toward the most promising compounds.
Computational Drug Discovery Methods
Virtual Screening & Molecular Docking
Performed large-scale virtual screening campaigns using molecular docking to identify potential drug candidates from chemical databases, followed by detailed binding mode analysis and interaction profiling.
Molecular Dynamics Simulations
Conducted extensive MD simulations to validate docking results, assess binding stability, and understand dynamic behavior of drug-target complexes under physiological conditions.
ADMET Property Prediction
Applied computational models to predict Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties of drug candidates, enabling early-stage optimization for drug-like characteristics.
Structure-Activity Relationship (SAR) Analysis
Developed quantitative structure-activity relationship (QSAR) models to predict biological activity and guide chemical modifications for improved potency and selectivity.
Research Impact & Outcomes
Successfully identified novel drug candidates through virtual screening campaigns, with several compounds advancing to experimental validation.
Developed robust computational workflows that reduced experimental screening time by 60-70% through effective prioritization of synthetic targets.
Applied ADMET prediction models to optimize drug-like properties early in the discovery process, improving success rates in subsequent experimental phases.
Contributed to structure-activity relationship understanding for multiple therapeutic targets, guiding medicinal chemistry optimization efforts.
Established collaborative frameworks between computational and experimental teams, enhancing the integration of computational methods in drug discovery pipelines.
Technologies & Methods
Related Publications
Exploration of SAM-I riboswitch inhibitors: In-Silico discovery of ligands to a new target employing multistage CADD approaches
Mohamed Elrefaiy, et al.
This study employs multistage Computer-Aided Drug Design (CADD) approaches to discover novel SAM-I riboswitch inhibitors, demonstrating the potential of computational methods in targeting RNA structures for therapeutic intervention.
Bioinspired computational design of lankacidin derivatives for improvement in antitumor activity
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This research focuses on the computational design of lankacidin derivatives using bioinspired approaches to enhance antitumor activity, providing insights into structure-activity relationships for improved therapeutic efficacy.
Computational prediction of the mode of binding of antitumor lankacidin C to tubulin
Mohamed Elrefaiy, et al.
This study presents computational predictions of the binding mode of antitumor lankacidin C to tubulin, elucidating the molecular basis for its anticancer activity through detailed structural analysis.